Use of the k-nearest neighbour and its analysis for fall detection on Systems on a Chip for multiple datasets
Fall of an elderly person often leads to serious injuries and death. Many falls occur in the home environment, and hence a reliable fall detection system that can raise alarms with minimum latency is a necessity. Wrist-worn accelerometer-based fall detection systems and multiple datasets are availab...
Uloženo v:
| Vydáno v: | Acta IMEKO Ročník 12; číslo 3; s. 1 - 11 |
|---|---|
| Hlavní autoři: | , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
18.09.2023
|
| ISSN: | 0237-028X, 2221-870X |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | Fall of an elderly person often leads to serious injuries and death. Many falls occur in the home environment, and hence a reliable fall detection system that can raise alarms with minimum latency is a necessity. Wrist-worn accelerometer-based fall detection systems and multiple datasets are available, but no attempt has been made to analyze the accuracy and precision. Wherever the comparison does exist, it has been run on a cloud. No analysis of the models, convergence, and dataset analysis on Systems on a Chip (SoCs) has ever been attempted. In this paper, we attempt to present why Machine Learning (ML) algorithms in their current state cannot be run on existing SoCs.
We have used Snapdragon 410c SoC to do our analytics. In this paper, we have used the kth-nearest neighbour to prove that ML cannot be directly run on SoCs. We have looked at the effect of distance metrics and neighbors as well as the effect of feature extraction on the accuracies and the latencies. In this paper, we establish the need for model compression and data pruning for fall detection using ML/Deep Learning algorithms on SoCs. We have done this by analyzing various datasets on varying architectural parameters. |
|---|---|
| AbstractList | Fall of an elderly person often leads to serious injuries and death. Many falls occur in the home environment, and hence a reliable fall detection system that can raise alarms with minimum latency is a necessity. Wrist-worn accelerometer-based fall detection systems and multiple datasets are available, but no attempt has been made to analyze the accuracy and precision. Wherever the comparison does exist, it has been run on a cloud. No analysis of the models, convergence, and dataset analysis on Systems on a Chip (SoCs) has ever been attempted. In this paper, we attempt to present why Machine Learning (ML) algorithms in their current state cannot be run on existing SoCs.
We have used Snapdragon 410c SoC to do our analytics. In this paper, we have used the kth-nearest neighbour to prove that ML cannot be directly run on SoCs. We have looked at the effect of distance metrics and neighbors as well as the effect of feature extraction on the accuracies and the latencies. In this paper, we establish the need for model compression and data pruning for fall detection using ML/Deep Learning algorithms on SoCs. We have done this by analyzing various datasets on varying architectural parameters. |
| Author | Anupama, K. R. Paliwal, Siddharth Agarwal, Himanish Nandi, Purab Jain, Arav |
| Author_xml | – sequence: 1 givenname: Purab surname: Nandi fullname: Nandi, Purab – sequence: 2 givenname: K. R. surname: Anupama fullname: Anupama, K. R. – sequence: 3 givenname: Himanish surname: Agarwal fullname: Agarwal, Himanish – sequence: 4 givenname: Arav surname: Jain fullname: Jain, Arav – sequence: 5 givenname: Siddharth surname: Paliwal fullname: Paliwal, Siddharth |
| BookMark | eNo9kMtqwzAUREVJoWmaTyjoB5zq4Ye8LKEvCHTRBrIzV_J1I2JbQVcp5O-bpKUwMLOYmcW5ZZMxjMjYvRQLJYXMH8Al8APuwuJbKq8XMjf1FZsqpWRmKrGZsKlQusqEMpsbNifyVhSyFLmqyykb1oQ8dDxtke-yESEiJT6i_9racIgcxpb7RCeH_kieeBci76DveYsJXfJh5Cd9HCnhQOcIfLn1-0tvOPTJ73vkLSQgTHTHrk9bwvmfz9j6-elz-Zqt3l_elo-rzMlS1xkoUboS8s5ap6UoC5urHI1RRrRFZatK6Q5bKHRlRFEbpVEjKAdoaqtbW-sZK35_XQxEEbtmH_0A8dhI0VywNf_Ymgu25oxN_wDJfGdT |
| ContentType | Journal Article |
| DBID | AAYXX CITATION |
| DOI | 10.21014/actaimeko.v12i3.1489 |
| DatabaseName | CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | CrossRef |
| DeliveryMethod | fulltext_linktorsrc |
| EISSN | 2221-870X |
| EndPage | 11 |
| ExternalDocumentID | 10_21014_actaimeko_v12i3_1489 |
| GroupedDBID | AAYXX ALMA_UNASSIGNED_HOLDINGS CITATION GROUPED_DOAJ OK1 |
| ID | FETCH-LOGICAL-c1639-a206c6a4fbbc31065b424e88280d57b7723feda5378059823e3ea2cae89b3db93 |
| ISSN | 0237-028X |
| IngestDate | Sat Nov 29 02:26:52 EST 2025 |
| IsDoiOpenAccess | false |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 3 |
| Language | English |
| License | https://creativecommons.org/licenses/by/4.0 |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c1639-a206c6a4fbbc31065b424e88280d57b7723feda5378059823e3ea2cae89b3db93 |
| OpenAccessLink | https://acta.imeko.org/index.php/acta-imeko/article/download/1489/2867 |
| PageCount | 11 |
| ParticipantIDs | crossref_primary_10_21014_actaimeko_v12i3_1489 |
| PublicationCentury | 2000 |
| PublicationDate | 2023-09-18 |
| PublicationDateYYYYMMDD | 2023-09-18 |
| PublicationDate_xml | – month: 09 year: 2023 text: 2023-09-18 day: 18 |
| PublicationDecade | 2020 |
| PublicationTitle | Acta IMEKO |
| PublicationYear | 2023 |
| SSID | ssib051604296 ssj0002140127 |
| Score | 2.2490351 |
| Snippet | Fall of an elderly person often leads to serious injuries and death. Many falls occur in the home environment, and hence a reliable fall detection system that... |
| SourceID | crossref |
| SourceType | Index Database |
| StartPage | 1 |
| Title | Use of the k-nearest neighbour and its analysis for fall detection on Systems on a Chip for multiple datasets |
| Volume | 12 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 2221-870X dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0002140127 issn: 0237-028X databaseCode: DOA dateStart: 20200101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 2221-870X dateEnd: 99991231 omitProxy: false ssIdentifier: ssib051604296 issn: 0237-028X databaseCode: M~E dateStart: 20120101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1La9tAEF7cNIdeSktb0lfYQ29GjrSrx-6xBIf0kbSUBHwTu9KqmMSykWQ3p_6f_svM7FobJYTSHApGLIM8SJ7P89rZGUI-8DgVFWbuIfIqgzjJdCBinQYxmAvJyzLSTtJfs9NTMZvJ76PRn_4szOYyq2txdSVX_1XUQANh49HZB4jbMwUCrEHocAWxw_WfBH_e-o3_i6DGFrVtN64xA4oJTL9ZoPpuJFhnWOEGdWk64waHow5xncxxqbBeY2Xv8-WHWFjaGtcFyjexLTo1_nQy_fLNZ5jxzIz1U9eN0jcZh_VKLdxRtMn4x8TTf6rml3LNtefYlqP1qerPatvqoFGbYZ6CcSyqGKpWIGUBeDMzZ3ksDZyTCPRxOLulj9kAd3ygXKOBlXYa-q7-Zzh5GK0bvPB8YS6Wk03E5hzMgZtTdLvf9h076KsTIS6yjHLPJrdscmTziDxmWSKxevDk97RXXUmUomVPfXqPYeDqBgb37-3Oj1nOB_c94MAzGrg4Z8_I021sQj86TD0nI1O_IAvAE11WFPBEPZ6oxxMFEVPAE-3xRAEnFPFEPZ4ofLZ4wqWiiCd7X48n2uPpJTk_mp4dHgfbGR1BAZ68DBQL0yJVcaV1AZFCmuiYxQbCNhGW8MeH2I1XplQJx9kZUjBuuFGsUEZIzUst-SuyUy9rs0eo1NgOkKeJhDBCRKHmFRhfaUDNhIWI1Gsy6X-ffOVaseR_FdWbh37hLXlyg9x3ZKdr1uY92S023bxt9m26Zt-K_RqoIocA |
| linkProvider | ISSN International Centre |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Use+of+the+k-nearest+neighbour+and+its+analysis+for+fall+detection+on+Systems+on+a+Chip+for+multiple+datasets&rft.jtitle=Acta+IMEKO&rft.au=Nandi%2C+Purab&rft.au=Anupama%2C+K.+R.&rft.au=Agarwal%2C+Himanish&rft.au=Jain%2C+Arav&rft.date=2023-09-18&rft.issn=0237-028X&rft.eissn=2221-870X&rft.volume=12&rft.issue=3&rft.spage=1&rft.epage=11&rft_id=info:doi/10.21014%2Factaimeko.v12i3.1489&rft.externalDBID=n%2Fa&rft.externalDocID=10_21014_actaimeko_v12i3_1489 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0237-028X&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0237-028X&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0237-028X&client=summon |